Automatic selectional preference acquisition for Latin verbs

ABSTRACT

We present a system that automatically induces Selectional Preferences (SPs) for Latin verbs from two treebanks by using Latin WordNet. Our method overcomes some of the problems connected with data sparseness and the small size of the input corpora. We also suggest a way to evaluate the acquired SPs on unseen events extracted from other Latin corpora.

References

1. }}E. Agirre and D. Martinez. 2001. Learning class-to-class selectional preferences. In Proceedings of the ACL/EACL 2001 Workshop on Computational Natural Language Learning (CoNLL-2001), pages 1--8.
2. }}A. Alishahi. 2008. A probabilistic model of early argument structure acquisition. Ph.D. thesis, Department of Computer Science, University of Toronto.
3. }}D. Bamman and G. Crane. 2006. The design and use of a Latin dependency treebank. In Proceedings of the Fifth International Workshop on Treebanks and Linguistic Theories, pages 67--78. ÚFAL MFF UK.
4. }}D. Bamman and G. Crane. 2008. Building a dynamic lexicon from a digital library. In Proceedings of the 8th ACM/IEEE-CS Joint Conference on Digital Libraries, pages 11--20.
5. }}L. Bentivogli, P. Forner, and and Pianta E. Magnini, B. 2004. Revising wordnet domains hierarchy: Semantics, coverage, and balancing. In Proceedings of COLING Workshop on Multilingual Linguistic Resources, pages 101--108.
6. }}S. Clark and D. Weir. 1999. An iterative approach to estimating frequencies over a semantic hierarchy. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora. University of Maryland, pages 258--265.
7. }}K. Erk. 2007. A simple, similarity-based model for selectional preferences. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, pages 216--223.
8. }}D. T. T. Haug and M. L. Jøndal. 2008. Creating a parallel treebank of the old Indo-European Bible translations. In Proceedings of Language Technologies for Cultural Heritage Workshop, pages 27--34.
9. }}H. Li and N. Abe. 1998. Generalizing case frames using a thesaurus and the MDL principle. Computational Linguistics, 24(2):217--244.
10. }}B. McGillivray and M. Passarotti. 2009. The development of the Index Thomisticus Treebank Valency Lexicon. In Proceedings of the Workshop on Language Technology and Resources for Cultural Heritage, Social Sciences, Humanities, and Education, pages 33--40.
11. }}B. McGillivray, M. Passarotti, and P. Ruffolo. 2009. The Index Thomisticus treebank project: Annotation, parsing and valency lexicon. TAL, 50(2):103--127.
12. }}B. McGillivray. 2009. Selectional Preferences from a Latin treebank. In Przepiórkowski A. Passarotti, M., S. Raynaud, and F. van Eynde, editors, Proceedings of the Eigth International Workshop on Treebanks and Linguistic Theories (TLT8), pages 131--136. EDUCatt.
13. }}S. Minozzi. 2009. The Latin Wordnet project. In P. Anreiter and M. Kienpointner, editors, Proceedings of the 15th International Colloquium on Latin Linguistics (ICLL), Innsbrucker Beitraege zur Sprachwissenschaft.
14. }}M. Passarotti and P. Ruffolo. forthcoming. Parsing the Index Thomisticus Treebank. some preliminary results. In P. Anreiter and M. Kienpointner, editors, Proceedings of the 15th International Colloquium on Latin Linguistics, Innsbrucker Beiträge zur Sprachwissenschaft.
15. }}M. Passarotti. 2007. Verso il Lessico Tomistico Biculturale. La treebank dell'Index Thomisticus. In R. Petrilli and D. Femia, editors, Atti del XIII Congresso Nazionale della Società di Filosofia del Linguaggio, pages 187--205.
16. }}P. Resnik. 1993. Selection and Information: A Class-Based Approach to Lexical Relationships. Ph.D. thesis, University of Pennsylvania.
17. }}M. Rooth, S. Riezler, D. Prescher, G. Carroll, and F. Beil. 1999. Inducing a semantically annotated lexicon via EM-based clustering. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, pages 104--111.

Index Terms

• Applied computing

  • Document management and text processing

• Computing methodologies

  • Artificial intelligence

    • Natural language processing